1. Field of the Invention
The present invention relates generally to a method for processing an image in which filtering effects noise reduction and improved smoothing while preserving edges of the image and enabling robust scale-space representation. The method can be applied to any kind of computed image having an underlying spatial or temporal domain, and also can effect reduced encoding of images.
2. Related Art
In the processing of an image an essential problem is to preserve the outline or edges of the image while at the same time removing or filtering out noise from the processing data. Whether the system is designed for either artificial vision (i.e., machine vision) or for signal processing, the essential difficulty is defining what the real signals are and, thus, the essential overall appearance of the image so as to ensure that in filtering out noise or restructuring of the image essential recognition characteristics are not lost. The distinction between "signal" and "noise" really depends on the level of description or scale one desires.
One approach taken to solving these problems in the past has been so-called Gaussian scale-space filtering, such as described in the article "Scale-space Filtering" by A. Witkin presented at the International Joint Conference Artificial Intellegence, Karlsruhe, Germany, 1983. According to this technique, a family of images are obtained by convolving an initial image I.sub.o with a Gaussian G(x,t). Accordingly, we now have I(x,t)=I.sub.o (x)*G(x,t) where variance t has been interpreted as the scale parameter. Considerable interest has been shown in this technique in the computer vision community.
A technique for smoothing data termed median filtering has also been proposed in a book entitled "Exploratory Data Analysis" by J. W. Tukey published by Addison-Wesley, Massachusetts in 1977. Median filtering has been used for both speech processing and image restoration, however, it has been found that in this technique of filtering insufficient smoothing has been obtained for the handling of non-impulsive noise. A generalized form referred to as an order statistic filter (OS) has found considerable use. Still other approaches to this problem have been attempted by so-called robust estimators which have been used to produce a modified trim mean filter. L-filters and M-filters have more direct motivation from statistical techniques.
Of these several known approaches to image processing, all are not completely satisfactory in being unable to provide one or more of the desired features of a fully successful system, namely, image edge preservation, noise reduction, improved smoothing of nonimpulsive noise, and robust scale-space representation.